I have been trying to normalize a very nested json file I will later analyze. What I am struggling with is how to go more than one level deep to normalize.
I went through the pandas.io.json.json_normalize documentation, since it does exactly what I want it to do.
I have been able to normalize part of it and now understand how dictionaries work, but I am still not there.
With below code I am able to get only the first level.
import json
import pandas as pd
from pandas.io.json import json_normalize
with open('authors_sample.json') as f:
d = json.load(f)
raw = json_normalize(d['hits']['hits'])
authors = json_normalize(data = d['hits']['hits'],
record_path = '_source',
meta = ['_id', ['_source', 'journal'], ['_source', 'title'],
['_source', 'normalized_venue_name']
])
I am trying to 'dig' into the 'authors' dictionary with below code, but the record_path = ['_source', 'authors'] throws me TypeError: string indices must be integers. As far as I understand json_normalize the logic should be good, but I still don't quite understand how to dive into a json with dict vs list.
I even went through this simple example.
authors = json_normalize(data = d['hits']['hits'],
record_path = ['_source', 'authors'],
meta = ['_id', ['_source', 'journal'], ['_source', 'title'],
['_source', 'normalized_venue_name']
])
Below is a chunk of the json file (5 records).
{u'_shards': {u'failed': 0, u'successful': 5, u'total': 5},
u'hits': {u'hits': [{u'_id': u'7CB3F2AD',
u'_index': u'scibase_listings',
u'_score': 1.0,
u'_source': {u'authors': None,
u'deleted': 0,
u'description': None,
u'doi': u'',
u'is_valid': 1,
u'issue': None,
u'journal': u'Physical Review Letters',
u'link': None,
u'meta_description': None,
u'meta_keywords': None,
u'normalized_venue_name': u'phys rev lett',
u'pages': None,
u'parent_keywords': [u'Chromatography',
u'Quantum mechanics',
u'Particle physics',
u'Quantum field theory',
u'Analytical chemistry',
u'Quantum chromodynamics',
u'Physics',
u'Mass spectrometry',
u'Chemistry'],
u'pub_date': u'1987-03-02 00:00:00',
u'pubtype': None,
u'rating_avg_weighted': 0,
u'rating_clarity': 0.0,
u'rating_clarity_weighted': 0.0,
u'rating_innovation': 0.0,
u'rating_innovation_weighted': 0.0,
u'rating_num_weighted': 0,
u'rating_reproducability': 0,
u'rating_reproducibility_weighted': 0.0,
u'rating_versatility': 0.0,
u'rating_versatility_weighted': 0.0,
u'review_count': 0,
u'tag': [u'mass spectra', u'elementary particles', u'bound states'],
u'title': u'Evidence for a new meson: A quasinuclear NN-bar bound state',
u'userAvg': 0.0,
u'user_id': None,
u'venue_name': u'Physical Review Letters',
u'views_count': 0,
u'volume': None},
u'_type': u'listing'},
{u'_id': u'7AF8EBC3',
u'_index': u'scibase_listings',
u'_score': 1.0,
u'_source': {u'authors': [{u'affiliations': [u'Punjabi University'],
u'author_id': u'780E3459',
u'author_name': u'munish puri'},
{u'affiliations': [u'Punjabi University'],
u'author_id': u'48D92C79',
u'author_name': u'rajesh dhaliwal'},
{u'affiliations': [u'Punjabi University'],
u'author_id': u'7D9BD37C',
u'author_name': u'r s singh'}],
u'deleted': 0,
u'description': None,
u'doi': u'',
u'is_valid': 1,
u'issue': None,
u'journal': u'Journal of Industrial Microbiology & Biotechnology',
u'link': None,
u'meta_description': None,
u'meta_keywords': None,
u'normalized_venue_name': u'j ind microbiol biotechnol',
u'pages': None,
u'parent_keywords': [u'Nuclear medicine',
u'Psychology',
u'Hydrology',
u'Chromatography',
u'X-ray crystallography',
u'Nuclear fusion',
u'Medicine',
u'Fluid dynamics',
u'Thermodynamics',
u'Physics',
u'Gas chromatography',
u'Radiobiology',
u'Engineering',
u'Organic chemistry',
u'High-performance liquid chromatography',
u'Chemistry',
u'Organic synthesis',
u'Psychotherapist'],
u'pub_date': u'2008-04-04 00:00:00',
u'pubtype': None,
u'rating_avg_weighted': 0,
u'rating_clarity': 0.0,
u'rating_clarity_weighted': 0.0,
u'rating_innovation': 0.0,
u'rating_innovation_weighted': 0.0,
u'rating_num_weighted': 0,
u'rating_reproducability': 0,
u'rating_reproducibility_weighted': 0.0,
u'rating_versatility': 0.0,
u'rating_versatility_weighted': 0.0,
u'review_count': 0,
u'tag': [u'flow rate',
u'operant conditioning',
u'packed bed reactor',
u'immobilized enzyme',
u'specific activity'],
u'title': u'Development of a stable continuous flow immobilized enzyme reactor for the hydrolysis of inulin',
u'userAvg': 0.0,
u'user_id': None,
u'venue_name': u'Journal of Industrial Microbiology & Biotechnology',
u'views_count': 0,
u'volume': None},
u'_type': u'listing'},
{u'_id': u'7521A721',
u'_index': u'scibase_listings',
u'_score': 1.0,
u'_source': {u'authors': [{u'author_id': u'7FF872BC',
u'author_name': u'barbara eileen ryan'}],
u'deleted': 0,
u'description': None,
u'doi': u'',
u'is_valid': 1,
u'issue': None,
u'journal': u'The American Historical Review',
u'link': None,
u'meta_description': None,
u'meta_keywords': None,
u'normalized_venue_name': u'american historical review',
u'pages': None,
u'parent_keywords': [u'Social science',
u'Politics',
u'Sociology',
u'Law'],
u'pub_date': u'1992-01-01 00:00:00',
u'pubtype': None,
u'rating_avg_weighted': 0,
u'rating_clarity': 0.0,
u'rating_clarity_weighted': 0.0,
u'rating_innovation': 0.0,
u'rating_innovation_weighted': 0.0,
u'rating_num_weighted': 0,
u'rating_reproducability': 0,
u'rating_reproducibility_weighted': 0.0,
u'rating_versatility': 0.0,
u'rating_versatility_weighted': 0.0,
u'review_count': 0,
u'tag': [u'social movements'],
u'title': u"Feminism and the women's movement : dynamics of change in social movement ideology, and activism",
u'userAvg': 0.0,
u'user_id': None,
u'venue_name': u'The American Historical Review',
u'views_count': 0,
u'volume': None},
u'_type': u'listing'},
{u'_id': u'7DAEB9A4',
u'_index': u'scibase_listings',
u'_score': 1.0,
u'_source': {u'authors': [{u'author_id': u'0299B8E9',
u'author_name': u'fraser j harbutt'}],
u'deleted': 0,
u'description': None,
u'doi': u'',
u'is_valid': 1,
u'issue': None,
u'journal': u'The American Historical Review',
u'link': None,
u'meta_description': None,
u'meta_keywords': None,
u'normalized_venue_name': u'american historical review',
u'pages': None,
u'parent_keywords': [u'Superconductivity',
u'Nuclear fusion',
u'Geology',
u'Chemistry',
u'Metallurgy'],
u'pub_date': u'1988-01-01 00:00:00',
u'pubtype': None,
u'rating_avg_weighted': 0,
u'rating_clarity': 0.0,
u'rating_clarity_weighted': 0.0,
u'rating_innovation': 0.0,
u'rating_innovation_weighted': 0.0,
u'rating_num_weighted': 0,
u'rating_reproducability': 0,
u'rating_reproducibility_weighted': 0.0,
u'rating_versatility': 0.0,
u'rating_versatility_weighted': 0.0,
u'review_count': 0,
u'tag': [u'iron'],
u'title': u'The iron curtain : Churchill, America, and the origins of the Cold War',
u'userAvg': 0.0,
u'user_id': None,
u'venue_name': u'The American Historical Review',
u'views_count': 0,
u'volume': None},
u'_type': u'listing'},
{u'_id': u'7B3236C5',
u'_index': u'scibase_listings',
u'_score': 1.0,
u'_source': {u'authors': [{u'author_id': u'7DAB7B72',
u'author_name': u'richard m freeland'}],
u'deleted': 0,
u'description': None,
u'doi': u'',
u'is_valid': 1,
u'issue': None,
u'journal': u'The American Historical Review',
u'link': None,
u'meta_description': None,
u'meta_keywords': None,
u'normalized_venue_name': u'american historical review',
u'pages': None,
u'parent_keywords': [u'Political Science', u'Economics'],
u'pub_date': u'1985-01-01 00:00:00',
u'pubtype': None,
u'rating_avg_weighted': 0,
u'rating_clarity': 0.0,
u'rating_clarity_weighted': 0.0,
u'rating_innovation': 0.0,
u'rating_innovation_weighted': 0.0,
u'rating_num_weighted': 0,
u'rating_reproducability': 0,
u'rating_reproducibility_weighted': 0.0,
u'rating_versatility': 0.0,
u'rating_versatility_weighted': 0.0,
u'review_count': 0,
u'tag': [u'foreign policy'],
u'title': u'The Truman Doctrine and the origins of McCarthyism : foreign policy, domestic politics, and internal security, 1946-1948',
u'userAvg': 0.0,
u'user_id': None,
u'venue_name': u'The American Historical Review',
u'views_count': 0,
u'volume': None},
u'_type': u'listing'}],
u'max_score': 1.0,
u'total': 36429433},
u'timed_out': False,
u'took': 170}
In the pandas example (below) what do the brackets mean? Is there a logic to be followed to go deeper with the [].
Each element in the ['state', 'shortname', ['info', 'governor']] is a path to an element to include, in addition to the selected rows. The 'counties' argument set what rows should be produced, and that second argument adds metadata that will be included with those rows.
Each is path, a list is a nested structure. In the example output you see the corresponding values in the state, shortname and info.governor columns.
In your example JSON, there are few nested lists to elevate with the first argument, like 'counties' did in the example. The only example in that datastructure is the nested 'authors' key; you'd have to extract each ['_source', 'authors'] path, after which you can add other keys from the parent object to augment those rows:
>>> json_normalize(raw, [['_source', 'authors']], ['_id', ['_source', 'journal'], ['_source', 'title']])
affiliations author_id author_name _id \
0 NaN 166468F4 a bowdoin van riper 7FDFEB02
1 NaN 81070854 jeffrey h schwartz 7FDFEB02
2 [Pennsylvania State University] 7E15BDFA roger l geiger 7538108B
_source.journal \
0 The American Historical Review
1 The American Historical Review
2 The American Historical Review
_source.title
0 Men Among the Mammoths: Victorian Science and ...
1 Men Among the Mammoths: Victorian Science and ...
2 Elizabeth Popp Berman. Creating the Market Uni...
So this is a dataframe of authors, with added metadata for each author (_id value, journal name and article title).
Note the path for the first argument; if you want to list a nested path you need to provide a list of paths (even if it is just one path); just ['_source', 'authors'] would look for two row sources, each a simple top-level name.
The second argument then pulls in the _id key from the outermost object, but the title and journal name are list paths, as these are nested too.
You can also have a look at the library flatten_json, which does not require you to write column hierarchies as in json_normalize:
from flatten_json import flatten
data = d['hits']['hits']
dict_flattened = (flatten(record, '.') for record in data)
df = pd.DataFrame(dict_flattened)
print(df)
来源：https://stackoverflow.com/questions/47242845/pandas-io-json-json-normalize-with-very-nested-json